npj Computational Materials最新文献_第4页

Shaping freeform nanophotonic devices with geometric neural parameterization 利用几何神经参数化技术塑造自由形状纳米光子器件

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-08-09 DOI: 10.1038/s41524-025-01752-w

Tianxiang Dai, Yixuan Shao, Chenkai Mao, Yu Wu, Sara Azzouz, You Zhou, Jonathan A. Fan

引用次数: 0

Range-separated hybrid functionals in full-potential LAPW using adaptively compressed exchange 使用自适应压缩交换的全电位LAPW中的距离分离混合函数

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-08-08 DOI: 10.1038/s41524-025-01733-z

Jānis Užulis, Aleksandr V. Sorokin, Andris Gulans

引用次数: 0

RAFFLE: active learning accelerated interface structure prediction RAFFLE：主动学习加速界面结构预测

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-08-08 DOI: 10.1038/s41524-025-01749-5

Ned Thaddeus Taylor, Joe Pitfield, Francis Huw Davies, Steven Paul Hepplestone

{"title":"RAFFLE: active learning accelerated interface structure prediction","authors":"Ned Thaddeus Taylor, Joe Pitfield, Francis Huw Davies, Steven Paul Hepplestone","doi":"10.1038/s41524-025-01749-5","DOIUrl":"https://doi.org/10.1038/s41524-025-01749-5","url":null,"abstract":"Interfaces between materials are critical to the performance of many devices, yet predicting their structure is computationally demanding due to the vast configuration space. We introduce RAFFLE, a software package for efficiently exploring low-energy interface configurations between arbitrary crystal pairs, enabling the generation of ensembles of interface structures. RAFFLE leverages physical insights and genetic algorithms to intelligently sample configurations, using dynamically evolving 2-, 3-, and 4-body distribution functions as generalised structural descriptors. These descriptors are refined through active learning to guide atom placement strategies. RAFFLE performs well across diverse systems, including bulk materials, intercalation compounds, and interfaces. It correctly recovers known bulk phases of aluminum and MoS2, and predicts stable phases in intercalation and grain-boundary systems. For Si∣Ge interfaces, it finds intermixed and abrupt structures to be similarly stable. By accelerating interface structure prediction, RAFFLE offers a powerful tool for materials discovery, enabling efficient exploration of complex configuration spaces.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"15 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144797312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advancing simulations of coupled electron and phonon nonequilibrium dynamics using adaptive and multirate time integration 利用自适应和多速率时间积分技术推进电子和声子耦合非平衡动力学模拟

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-08-07 DOI: 10.1038/s41524-025-01738-8

Jia Yao, Ivan Maliyov, David J. Gardner, Carol S. Woodward, Marco Bernardi

{"title":"Advancing simulations of coupled electron and phonon nonequilibrium dynamics using adaptive and multirate time integration","authors":"Jia Yao, Ivan Maliyov, David J. Gardner, Carol S. Woodward, Marco Bernardi","doi":"10.1038/s41524-025-01738-8","DOIUrl":"https://doi.org/10.1038/s41524-025-01738-8","url":null,"abstract":"Electronic structure calculations in the time domain provide a deeper understanding of nonequilibrium dynamics in materials. The real-time Boltzmann equation (rt-BTE), used in conjunction with accurate interactions computed from first principles, has enabled reliable predictions of coupled electron and lattice dynamics. However, the timescales and system sizes accessible with this approach are still limited, with two main challenges being the different timescales of electron and phonon interactions and the cost of computing collision integrals. As a result, only a few examples of these calculations exist, mainly for two-dimensional (2D) materials. Here we leverage adaptive and multirate time integration methods to achieve a major step forward in solving the coupled rt-BTEs for electrons and phonons. Relative to conventional (non-adaptive) time-stepping, our approach achieves a 10x speedup for a target accuracy, or greater accuracy by 3–6 orders of magnitude for the same computational cost, enabling efficient calculations in both 2D and bulk materials. This efficiency is showcased by computing the coupled electron and lattice dynamics in graphene up to ~100 ps, as well as modeling ultrafast lattice dynamics and thermal diffuse scattering maps in bulk materials (silicon and gallium arsenide). In addition to improved efficiency, our adaptive method can resolve the characteristic rates of different physical processes, thus naturally bridging different timescales. This enables simulations of longer timescales and provides a framework for modeling multiscale dynamics of coupled degrees of freedom in matter. Our work opens new opportunities for quantitative studies of nonequilibrium physics in materials, including driven lattice dynamics with phonons coupled to electrons, spin, and other degrees of freedom.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"27 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144792480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An interleaved physics-based deep-learning framework as a new cycle jumping approach for microstructurally small fatigue crack growth simulations 基于交错物理的深度学习框架作为微结构小疲劳裂纹扩展模拟的新循环跳跃方法

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-08-05 DOI: 10.1038/s41524-025-01741-z

Vignesh Babu Rao, Ashley D. Spear

{"title":"An interleaved physics-based deep-learning framework as a new cycle jumping approach for microstructurally small fatigue crack growth simulations","authors":"Vignesh Babu Rao, Ashley D. Spear","doi":"10.1038/s41524-025-01741-z","DOIUrl":"https://doi.org/10.1038/s41524-025-01741-z","url":null,"abstract":"Conventional fracture mechanics asserts that the relevant physics governing small crack growth occurs near the crack front. However, for fatigue, computing these physics for each crack-growth increment over the entire microstructurally small crack regime is computationally intractable. Properly trained deep-learning surrogate models can massively accelerate fatigue crack-growth predictions by virtually propagating an initial crack using micromechanical fields corresponding to just the initially cracked microstructure. As the predicted crack front advances, however, the fields no longer reflect relevant near-crack-front physics, leading to error and uncertainty accumulation. To address this, we present an interleaved physics-based deep-learning (PBDL) framework, where updates to the crack representation in the physics-based model are triggered intermittently using model uncertainty, thereby updating micromechanical fields passed to the deep-learning model. We show that this framework, representing a novel cycle-jumping approach, effectively limits error accumulation in history-dependent fatigue crack evolution and forms a template for other time-series applications in materials.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"126 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144787325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Artificial intelligence can recognize metallic glasses in vast compositional space with sparse data 人工智能可以用稀疏的数据在广阔的构图空间中识别金属玻璃

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-08-05 DOI: 10.1038/s41524-025-01753-9

Weijie Xie, Yitao Sun, Chao Wang, Mingxing Li, Fucheng Li, Yanhui Liu

引用次数: 0

Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry 材料图库（MatGL），一个用于材料科学和化学的开源图深度学习库

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-08-05 DOI: 10.1038/s41524-025-01742-y

Tsz Wai Ko, Bowen Deng, Marcel Nassar, Luis Barroso-Luque, Runze Liu, Ji Qi, Atul C. Thakur, Adesh Rohan Mishra, Elliott Liu, Gerbrand Ceder, Santiago Miret, Shyue Ping Ong

{"title":"Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry","authors":"Tsz Wai Ko, Bowen Deng, Marcel Nassar, Luis Barroso-Luque, Runze Liu, Ji Qi, Atul C. Thakur, Adesh Rohan Mishra, Elliott Liu, Gerbrand Ceder, Santiago Miret, Shyue Ping Ong","doi":"10.1038/s41524-025-01742-y","DOIUrl":"https://doi.org/10.1038/s41524-025-01742-y","url":null,"abstract":"Graph deep learning models, which incorporate a natural inductive bias for atomic structures, are of immense interest in materials science and chemistry. Here, we introduce the Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry. Built on top of the popular Deep Graph Library (DGL) and Python Materials Genomics (Pymatgen) packages, MatGL is designed to be an extensible “batteries-included” library for developing advanced model architectures for materials property predictions and interatomic potentials. At present, MatGL has efficient implementations for both invariant and equivariant graph deep learning models, including the Materials 3-body Graph Network (M3GNet), MatErials Graph Network (MEGNet), Crystal Hamiltonian Graph Network (CHGNet), TensorNet and SO3Net architectures. MatGL also provides several pre-trained foundation potentials (FPs) with coverage of the entire periodic table, and property prediction models for out-of-box usage, benchmarking and fine-tuning. Finally, MatGL integrates with PyTorch Lightning to enable efficient model training.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"5 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144778481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comparative convolutional neural networks for perovskite solar cell PCE predictions 比较卷积神经网络用于钙钛矿太阳能电池PCE预测

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-08-04 DOI: 10.1038/s41524-025-01744-w

Milan Harth, D. Kishore Kumar, Said Kassou, Kenza El Idrissi, Ritesh Kant Gupta, Yonatan Daniel, Ofry Makdasi, Iris Visoly-Fisher, Alessio Gagliardi

{"title":"Comparative convolutional neural networks for perovskite solar cell PCE predictions","authors":"Milan Harth, D. Kishore Kumar, Said Kassou, Kenza El Idrissi, Ritesh Kant Gupta, Yonatan Daniel, Ofry Makdasi, Iris Visoly-Fisher, Alessio Gagliardi","doi":"10.1038/s41524-025-01744-w","DOIUrl":"https://doi.org/10.1038/s41524-025-01744-w","url":null,"abstract":"Imaging offers a fast and accessible means for spatial characterization of halide perovskite photovoltaic materials, yet extracting optoelectrical properties—such as power conversion efficiency (PCE)—remains challenging. This study presents a deep learning methodology that correlates optical reflective images of perovskite solar cells with their PCE by focusing on image differences rather than absolute visual features. The approach predicts relative changes in PCE by comparing images of the same device in different states (e.g., before and after encapsulation) or against a reference image. This comparative technique significantly outperforms traditional methods that attempt to directly infer PCE from a single image. Furthermore, it demonstrates high effectiveness in low-data regimes, using only 115 samples. By leveraging convolutional neural networks (CNNs) trained on small datasets, the method offers an adaptable and scalable solution for device characterization. Overall, the comparative approach enhances the accuracy and applicability of machine vision in perovskite solar cell analysis.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"156 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144778484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Understanding piezocatalysis of barium titanate in solution from quantum-continuum-electrochemical theory 从量子连续电化学理论理解溶液中钛酸钡的压电催化作用

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-08-04 DOI: 10.1038/s41524-025-01746-8

Xiangyu Zhu, Cheng Zhan, Erjun Kan

引用次数: 0

DPmoire: a tool for constructing accurate machine learning force fields in moiré systems DPmoire：用于在moirearning系统中构建精确的机器学习力场的工具

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-08-01 DOI: 10.1038/s41524-025-01740-0

Jiaxuan Liu, Zhong Fang, Hongming Weng, Quansheng Wu

引用次数: 0